Aerosol activity of forming cloud droplets affects cloud micro-physics and indicates aerosol hygroscopicity. Application of the cloud condensation nuclei (CCN) counter (CCNC) is an important method for studying CCN-active particles. Factors impacting measurements using CCNC are analyzed by laboratory study. In CCNC, variations of supre-saturations (SS) are found to be proportional to variations of the pressure and the flow-rate. Temperature conditions, under which CCNC is working, exhibit nonlinear influnces on SSs in CCNC. Varying aerosol losses with particle size due to the pressure adjustment is found when differential pressure is higher than 300 hPa. Underestimation of CCN number concentration is found when particle number concentration is higher than 10000/cm³ and SS in CCNC is lower than 0.2%. This is because only part of CCN-active particles can activate due to the vapor depletion in CCNC. The results and suggestions provide instructions for CCN measurment and would help improving quality control and analysis of CCN data.

A modified method for calibrating the supersaturations in the chambers of DMT Cloud Condensation Nuclei Counter is introduced, which decreases the uncertainty in the results of the calibration. In the new method, the complete size-resolved activation ratio for ammonium sulfate is observed, and the activation critical diameter is yielded via fitting the size-resolved activation ratio with two Gauss error functions. With the new method, users can do the calibration periodically to guarantee high-quality observations.

Two methods are applied to detect non-stationarity in time series. One is the run-test (RT) method, which depends on statistic theory; the other is a graphical method called space time-index (STI), which is a dynamical method. Three different RT methods and STI method are given to detect the non-stationarity in artificial time series with known non-stationarity and experimental turbulence time series measured in atmospheric boundary layer. The results show that the RT method which has lower accuracy is simpler and more efficient than STI. Besides comparing the merits of the STI and RT methods, the results also show that the variance RT method is the most effective to detect the non-stationarity in experimental turbulence time series among the three ones of RT methods.